KGLiDS is a knowledge graph-based platform for linked data science.
KGQAn aims to develop a data science chatbot that can answer questions from different knowledge graphs without prior knowledge of the graphs. We are developing KGQAn as a chat assistant that guides data scientists to easily explore data science projects’ findings.
KGNet aims to develop an embedding as a service (EaaS) that can extend different RDF engines to support various embedding techniques. EaaS is a step forward to extend RDF engines to support embedding-based operators to explore better KGs based on semantics and classification models.
KGpip is scalable AutoML approach based on a novel formulation for the AutoML problem as a graph generation problem. In KGpip, we train a novel meta-learning on top of of our knowledge graph for linked data science to pose learner and pre-processing selection as a generation of different graphs representing ML pipelines. For more information, please read our KGpip paper
AlphaBot is a weak supervision-based approach to improve chatbots for code repositories. We evaluate AlphaBot using a dataset that composes of 749 queries representing 52 intents. Our results show that AlphaBot helps chatbot practitioners to boost the NLU’s performance at early releases of their chatbots (i.e., fewer training queries). In particular, we find that our approach increases the NLU’s performance up to 44% compared to the baseline. Also, the results show that AlphaBot annotates, on average, 99% of queries correctly.
This project aims at developing a feature store for data science projects. Discovering features is one of the applications on top of our knowledge graph for linked data science.
This project aims at developing a platform for detecting advanced persistent threats (APT) based on knowledge graph technologies. Our approach utilizes graph neural network and semantic graph similarity to detect attack scenarios in a provenance graph of network logs.
This project aims at developing a deep active learning platform for triple extraction tasks from the English text. Our platform automates the dataset annotation process required for training models for question understanding or knowledge graph construction.